arXiv：cs.LG· Xiaojing Du, Jiuyong Li, Lin Liu, Debo Cheng, Jixue Liu, Thuc Duy Le·· 3 小时前AI 评分30
符号网络中的同伴效应：区分正负关系的影响
Peer Effects in Signed Networks: Separating Influence Through Positive and Negative Ties
AI 导读
研究提出 SiDE（Signed-exposure Doubly robust Estimator），通过区分正向与负向关系来估计网络干预中的同伴效应，并给出识别公式与双重稳健性证明。在六个真实符号网络上的半合成实验显示该估计器能准确估计效应，并检验了区间覆盖的局限。对已发表学校实验数据的探索性再分析发现，佩戴手环的相处时间关系存在正向同伴效应估计，但多重比较校正后四种效应的区间均包含零。
正文
Abstract:Evaluating network interventions requires understanding how treatment affects people through their social relationships. Counting treated neighbors without distinguishing supportive and antagonistic ties can conceal opposing influences. We define effects through positive and negative ties, their interaction, and a sign-composition effect of reallocating treatment between the two types at a fixed total, and give their identification formulas. Under sign-blind assignment, we show how ignoring signs mixes the effects of the two tie types. We propose SiDE (Signed-exposure Doubly robust Estimator), which combines sign-specific outcome models with exposure probabilities induced by individual treatment assignment. We establish double robustness of its score and assess approximate intervals that account for overlapping neighborhoods. Semi-synthetic experiments on six real signed networks demonstrate accurate effect estimation and examine the limits of interval coverage. An exploratory reanalysis of published school-experiment data yields a positive estimate of the peer effect through spend-time ties on wristband wearing, but the intervals for all four effects include zero after adjustment for multiple comparisons. This framework can inform network intervention design by showing when influences through the two tie types reinforce or offset one another.
| Comments: | 12 pages |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.02872 [cs.LG] |
| (or arXiv:2610.02872v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02872 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xiaojing Du [view email]
[v1]
Fri, 2 Oct 2026 06:10:47 UTC (57 KB)
来源：arXiv：cs.LG · arxiv.org